P08.03: Customised fetal growth charts by quantile regression analysis: a cross-sectional multicentric Italian study

2015 ◽  
Vol 46 ◽  
pp. 147-147
Author(s):  
N. Volpe ◽  
G. Rizzo ◽  
L. Cariello ◽  
R. Ludovica ◽  
A. Dall'Asta A ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Fetal Growth ◽  
Growth Charts ◽  
Cross Sectional ◽  
Italian Study ◽  
Quantile Regression Analysis

Customized Fetal Growth Charts for Parents' Characteristics, Race, and Parity by Quantile Regression Analysis

2016 ◽  
Vol 35 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Tullio Ghi ◽  
Luisa Cariello ◽  
Ludovica Rizzo ◽  
Enrico Ferrazzi ◽  
Enrico Periti ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Fetal Growth ◽  
Growth Charts ◽  
Quantile Regression Analysis

scholarly journals Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa

2021 ◽  
Vol 14 (1) ◽  
pp. 409-416
Author(s):  
Delson Chikobvu ◽  
Lyness Matizirofa
Keyword(s):  
South Africa ◽  
Regression Analysis ◽  
Quantile Regression ◽  
Age Groups ◽  
Female Gender ◽  
Public Hospitals ◽  
Cross Sectional ◽  
Black Race ◽  
Modifiable Factors ◽  
Quantile Regression Analysis

Background: Stroke is the second largest cause of mortality and long-term disability in South Africa (SA). Stroke is a multifactorial disease regulated by modifiable and non-modifiable predictors. Little is known about the stroke predictors in SA, particularly modifiable and non-modifiable. Identification of stroke predictors using appropriate statistical methods can help formulate appropriate health programs and policies aimed at reducing the stroke burden. This study aims to address important gaps in stroke literature i.e., identifying and quantifying stroke predictors through quantile regression analysis. Methods: A cross-sectional hospital-based study was used to identify and quantify stroke predictors in SA using 35730 individual patient data retrieved from selected private and public hospitals between January 2014 and December 2018. Ordinary logistic regression models often miss critical aspects of the relationship that may exist between stroke and its predictors. Quantile regression analysis was used to model the effects of each predictor on stroke distribution. Results: Of the 35730 cases of stroke, 22183 were diabetic. The dominant stroke predictors were diabetes, hypertension, heart problems, the female gender, higher age groups and black race. The age group 55-75 years, female gender and black race, had a bigger effect on stroke distribution at the lower upper quantiles. Diabetes, hypertension and cholesterol showed a significant impact on stroke distribution (p < 0.0001). Conclusion: Most strokes are attributable to modifiable factors. Study findings will be used to raise awareness of modifiable predictors to prevent strokes. Regular screening and treatment are recommended for high-risk individuals with identified predictors in SA.

scholarly journals Association between leukocyte telomere length and sex by quantile regression analysis

2021 ◽  
Author(s):  
Fernanda Gutierrez-Rodrigues ◽  
Raquel M. Alves-Paiva ◽  
Natália F. Scatena ◽  
Edson Z. Martinez ◽  
Priscila S. Scheucher ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Telomere Length ◽  
Leukocyte Telomere Length ◽  
Quantile Regression Analysis

Determinants of liquidity in Malaysian SMEs: a quantile regression approach

2018 ◽  
Vol 67 (9) ◽  
pp. 1566-1584 ◽  
Author(s):  
Shaista Wasiuzzaman
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Emerging Market ◽  
Developed Countries ◽  
Data Availability ◽  
Firm Level ◽  
Liquidity Management ◽  
Ols Regression ◽  
Content Type ◽  
Quantile Regression Analysis

PurposeThe management of liquidity has always been seen as a critical but often ignored issue in finance. Despite the abundance of studies on liquidity management, these studies mainly focus on developed countries and on large firms. Liquidity is critical for the small firm but studies on liquidity management in small and medium enterprises (SMEs) are lacking. The purpose of this paper is to examine the firm-level determinants of liquidity of SMEs in Malaysia.Design/methodology/approachData are collected for a total of 986 small firms in Malaysia from 2011 to 2014, resulting in a total of 2,683 observations. Firm-specific variables and the effect of the economy are considered as the possible determinants of liquidity. Ordinary least squares (OLS) regression analysis with standard errors adjusted for firm-level clustering and quantile regression analysis are used for this purpose.FindingsAnalysis using OLS regression technique indicates that a firm’s profitability, its growth, asset tangibility, size, age and firm status are significant factors in influencing its liquidity decision. Leverage and economic condition are not found to have any significant influence on liquidity. However, quantile regression analysis provides a different picture especially for SMEs with liquidity at the quantile levels ofθ=0.10 and 0.90. Atθ=0.10, only profitability, tangibility and firm status are significant, while atθ=0.90, tangibility, size, firm status and, to some extent, age are significant in influencing liquidity levels.Originality/valueTo the author’s knowledge, this is the first study analyzing the liquidity decision of SMEs in an emerging market such as Malaysia. Most studies on liquidity management of SMEs are focused on developed countries due to data availability but these studies are also only a handful. Additionally, this study uses quantile regression analysis which highlights the need to analyze financial decisions at different levels rather than at the aggregate level as done in OLS regression analysis.

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